DataFlux

Diagnose your data at the outset

Companies spend billions of dollars each year implementing enterprise applications or integrating customer or product data, and industry estimates show these projects fail or go over budget 65-75 percent of the time. Beginning a data-driven initiative without first understanding the data is like fixing a car without understanding the problems inside the engine. To fix the engine, you have to understand the depth and breadth of the problem.

Similarly, data improvement efforts must start with an understanding of the integrity of the data. The first phase of DataFlux's methodology is data profiling (also known as "data discovery"). With data profiling, you can:

• Discover the quality, characteristics and potential problems of information before beginning data-driven projects
• Drastically reduce the time and resources required to find problematic data
• Allow business analysts and data stewards to have more control on the maintenance and management of enterprise data
• Catalog and analyze metadata and discover metadata relationships

With DataFlux, data profiling is completely integrated into the full data quality integration solution. So, as you uncover non-standard or duplicate data, you can immediately develop business rules to correct these issues. This shortens the time between data discovery and data correction, helping provide a faster time-to-value from your data profiling efforts.

The first step to building better data

As the initial step to any data quality program, data profiling provides a wealth of information about the data that you have.  DataFlux data profiling solutions automatically identify data quality issues in a variety of ways, including:

• Basic statistics, frequencies, ranges and outliers
• Identify multiple spellings of the same content
• Discover and validate data patterns and formats
• Numeric range analysis
• Identify and validate redundant data and primary/foreign key relationships across data sources
• Identify duplicate name and address and non-name and address information
• Validate data specific business rules within a single record or across sources

The next step: Data Quality

Data profiling provides an analysis of the data problems you face. The next phase - data quality - starts the process of building better data.